In this study we test the idea of using a graphics processing unit (GPU) as an em- bedded co-processor for real-time speed-limit sign detection. We implement a sys- tem that operates in real-time within the computational limits of contemporary em- bedded general-purpose processors. The input to the system is a set of grayscale videos recorded from a camera mounted in a vehicle. We implement a new tech- nique to realize the radial symmetry transform e+-ciently using rendering primi- tives accelerated by graphics hardware to detect the location of speed-limit sign candidates. The system reaches up to 88% detection rate and runs at 33 frames per second on video sequences with VGA (640x480) resolution on an embedded system with an Intel Atom 230 @ 1.67 GHz and a Nvidia GeForce 9200M GS.

In this study we test the idea of using a graphics processing unit (GPU) as an embedded co-processor for realtime detection of European Union (EU) speed-limit signs. The input to the system is a set of grayscale videos recorded from a forward-facing camera mounted in a vehicle. We introduce a new technique for implementing the radial symmetry detector (RSD) efficiently using the native rendering capabilities of a GPU. The technique maps the algorithms to the hardware such that the detection of speed-limit sign candidates is significantly accelerated. The system reaches up to 88% detection rate and runs at 33 frames per second on video sequences with VGA (640x480) resolution on an embedded system with an Intel Atom 230 @ 1.67 GHz CPU and a NVIDIA GeForce 9400M GS GPU.

We present a template-based pipeline that performs real-time speed-limit-sign recognition using an embedded system with a low-end GPU as the main processing element. Our pipeline operates in the frequency domain, and uses nonlinear composite filters and a contrast-enhancing preprocessing step to improve its accuracy. Running at interactive rates, our system achieves 90% accuracy over 120 EU speed-limit signs on 45 minutes of video footage, superior to the 75% accuracy of a non-real-time GPU-based SIFT pipeline.